Computational Vision Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

Hello Peers, Today we are going to share all week’s assessment and quizzes answers of the Computational Vision course launched by Coursera totally free of cost✅✅✅. This is a certification course for every interested student.

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Coursera, India’s biggest learning platform launched millions of free courses for students daily. These courses are from various recognized universities, where industry experts and professors teach in a very well manner and in a more understandable way.

Here, you will find Computational Vision Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Computational Vision from Coursera Free Certification Course.

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About Computational Vision Course

In this course, we will expand on vision as a cognitive problem space and explore models that address various vision tasks. We will then explore how the boundaries of these problems lead to a more complex analysis of the mind and the brain and how these explorations lead to more complex computational models of understanding.

WHAT YOU WILL LEARN

  • Apply various models of human and machine vision and discuss their limitations.
  • Demonstrate the geon model of object recognition and its limitations.
  • Argue the benefits and drawbacks of the symbolist and visualise perspectives of mental imagery.
  • Recognize the single-layer and multi-layer perceptron neural network models of artificial intelligence.

Course Apply Link – Computational Vision

Computational Vision Quiz Answers

Week 1 Quiz Answers

Quiz 1: Vision Overview

Q1. The human eye, to a first approximation, works like which of the following?

  • A​ notebook
  • A​ pinhole camera
  • A​ recording device
  • A​ sketch Artist

Q2. The “piano-in-the-mirror” example illustrates which of the following ideas?

  • It is impossible in principle to recover a unique three-dimensional structure from a two-dimensional projection.
  • The eye’s retina is unreliable in its operation.
  • Mirrors pose a particular challenge to the human vision system.
  • Optical illusions reveal the strengths of the human vision system.

Q3. Which of these does not represent a potential complicating factor for our “pixel-array” portrait of the retina?

  • There are many wavelengths that the eye is not responsive to.
  • Color vision can provide useful information for interpreting an image.
  • The fovea has higher resolution than the periphery of the retina, so our “evenly distributed array” portrait is inaccurate.
  • Binocular vision can provide useful information for interpreting an image.

Q4. As a very first step in treating vision as a computational problem, we can think of a retinal image as:

  • A photograph.
  • A small copy of the object being attended to.
  • An array of pixels, where each pixel denotes a light intensity value.
  • A line sketch of the object being attend to.

Q5. Despite the simplicity of our first model of vision – interpreting black and white photos – it is not entirely unfair because:

  • Binocular vision is generally of little use.
  • It is, after all, a task that we as human beings are capable of.
  • Most scenes in real life do not involve information such as motion.
  • Many animals have limited color vision.

Q6. Optical illusions are useful tools for studying the computational view of vision because:

  • They are entertaining illustrations of how odd the visual world is.
  • They highlight “gaps” in our vision algorithms – situations where the algorithms give the wrong answers.
  • They show that we are not as good at “seeing” as we think.
  • They show that our color vision is faulty.

Week 2

Quiz 1: Edges

Q1. When considering an image as a 2D array of pixels, what denotes an edge?

  • A line of pixels with similar intensity.
  • A high average intensity level among surrounding pixels.
  • A very high intensity pixel.
  • A line of pixels with adjacent pixels with very different intensity levels (high and low values).

Q2. If our convolution function is centered on a low-intensity (dark) pixel along a dark-to-light transition, what kind of value will the function output?

  • A positive number.
  • A negative number.
  • We don’t have enough information to determine the answer.
  • 0​

Q3. Which statement best represents the relationship between human vision and the convolution function approach to edge detection?

  • Photoreceptors are sensitive to transitions between high and low intensity of light, like a convolution function, rather than just intensity, like a pixel.
  • The retinal ganglion cells perform a similar function to the convolution function, looking for differences in signal from an area of photoreceptor cells.
  • Retinal ganglion cells do not use a convolution approach, instead, they take an average of inputs from local photoreceptors then poll nearby ganglion cells to look for differences.
  • The convolution approach is not a good representation of human vision.

Quiz 2: Geons

Q1. Suppose, as a rough estimate, we say that there are 20 distinct geons used for object recognition; and each geon can come in 5 classifiable qualitative sizes (tiny, small, moderate, large, huge); and a pair of geons can be placed in 10 distinct qualitative relations (geon A on top of geon B; geon A to upper left of geon B; geon A to the left of geon B; and so forth).

How many distinct two-geon objects do we have in the space described above?

Enter answer here

Q2. Now, suppose we add a third geon, geon C. Again, each geon comes in 20 varieties and 5 sizes. We’ll start by creating a two-geon pair of A and B just like in Question 1 above; then, we decide which of A or B the third geon (C) will be adjacent to, and then we place geon C beside either A or B in one of the 10 allowed relations. How many distinct three-geon objects do we have in this space?

Enter answer here

Q3. Are these numbers (as an estimate of the “dictionary size” of potential two-geon and three-geon objects) significantly bigger, significantly smaller, or comparable to the actual size of our object vocabulary in English as discussed in lecture?

  • much ​bigger
  • much s​maller
  • about the s​ame

Week 3

Quiz 1: Mental Imagery

Q1. Explain the significance of the following experiment (described in lecture and/or the reading from Finke) to the debates surrounding mental imagery. Does the experiment better support the “symbolist” camp or “visualist / pictoralist” camp as discussed in lecture?

Shepard-Metzler “mental rotation” experiment.

  • S​ymbolist
  • V​isualist

Q2. Ambiguous figure (duck/rabbit) mental imagery experiment

  • S​ymbolist
  • V​isualist

Q3. Finke experiment (mental imagery and resolution of grids of lines)

  • S​ymbolist
  • V​isualist

Q4. Mental imagery and the McCollough effect

  • S​ymbolist
  • V​isualist

Week 4

Quiz 1: Convolution Problem

Q1. Consider the following matrix representation of a 4 pixel by 4 pixel black and white image, which we will call A:

1101
0101
1110
1100

And the edge detection matrix B:

-1-1-1
-18-1
-1-1-1

If we convolve matrix A and Matrix B, what are the values in the resulting matrix?

For this answer, write your answer in the form:

A, B, C, D

Where each letter is replaced with the numeric value that would be found in this matrix representation:

AB
CD

Conclusion

Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of the Computational Vision Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.

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